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Consider a hypothetical banking application that handles transactions one after another. These transactions may be checks, deposits, and a large number of more obscure transactions. When the program executes, the actual data may consist of clearing tens of thousands of checks without processing a single deposit and without processing a single check with a fraudulent account number. An adaptive optimizer would compile assembly code to optimize for this common case. If the system then started processing tens of thousands of deposits instead, the adaptive optimizer would recompile the assembly code to optimize the new common case. This optimization may include inlining code.
Examples of adaptive optimization include [[HotSpot (virtual machine)|HotSpot]] and HP's [[Dynamo project|Dynamo system]].<ref>[https://arstechnica.com/reviews/1q00/dynamo/dynamo-1.html HP's Dynamo]</ref>
In some systems, notably the [[Java Virtual Machine]]{{Citation needed|date=June 2011}}, execution over a range of [[Java bytecode|bytecode instructions]] can be [[Reversible_computing|provably reversed.]] This allows an adaptive optimizer to make risky assumptions about the code. In the above example, the optimizer may assume all transactions are checks and all account numbers are valid. When these assumptions prove incorrect, the adaptive optimizer can 'unwind' to a valid state and then interpret the byte code instructions correctly.
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